1. Field of the Invention
The present invention relates to a learning method of a neural network. More particularly, the present invention relates to a learning method of a neural network in which, for example, for each sample, the similarity for each phoneme category is obtained in advance according to the distance between that sample and a sample included in each phoneme category, the similarity is applied as a target signal to a neural network, and the neural network learns by the back-propagation.
2. Description of the Background Art
In a conventional learning method of a neural network, the neural network learns categories of learning samples by the back-propagation with "0" or "1". In the learning method, categories are learned directly, so that the neural network can learn to have considerably high category identifying capability for a learning set with a feature of clearly determining if it is in the category or not with "1" or "0" respectively.
On the other hand, however, when identifying a category for a data set with somewhat different feature, such as speaking rate or speaking style in speech recognition, from that of the learning set, the category identifying capability considerably decreases. Furthermore, when a neural network makes an identification mistake once, its result is outputted as "1", "0". That is, it had a disadvantage that, for example, when "white" should be determined to be "0" and "black" should be determined to be "1" it happens to make a great error, i.e. to make a determination of "1" for "white" and for "black". Furthermore, since the determinations are made with "0", "1" it has a disadvantage that, for example, in combination with higher level information such as a language model, in speech recognition and in the character recognition, information lack is likely to happen in an output of the neural network which prevents improvement of the recognization rate using the higher level information. As a result, it also had a disadvantage that a high recognization rate could not be obtained in overall system.